Advanced noise reduction in digital cameras

ABSTRACT

A noise reduction apparatus for digital cameras is presented that includes groups of one or more connected non-linear filter units. Each of the filter unit groups are driven by decimated input image data at a different level of decimation and the output of at least one of these filter unit groups serves as one of a plurality of inputs to another filter unit group driven at a different decimation level. Filtered image data from one or more filter unit groups is adaptively combined in response to one or more image metrics related to one or more local regional image characteristics.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation-in-part of co-pending U.S. patent applicationSer. No. 13/160,775, filed on Jun. 15, 2011 (to be abandoned), which isa continuation of U.S. patent application Ser. No. 11/754,202, filed onMay 25, 2007 (now U.S. Pat. No. 7,983,503), which are incorporatedherein by reference.

BACKGROUND

This application relates to image processing in digital cameras andother electronic digital image acquisition devices, and particularly totechniques for improving noise reduction techniques for such images.

Images obtained by digital cameras and other imaging systems containrandom noise, which typically grows stronger as the ISO sensitivitygrows higher. Noise reduction in digital cameras is becomingincreasingly important and problematic because of several trends in thedigital camera market which result in lower Signal to Noise Ratios(SNR), including the increasing of sensor resolution by reducing thepixel size and the providing better image quality at higher ISOsensitivities, which enables capture of images in low light conditions.

Prior art approaches typically effect noise reduction by either applyingedge preserving filters on the image or suppressing chromaticcomponents. Applying edge-preserving filters on the image, such asmedian filters, bilateral filters and others, are well known in the art.The difficulty encountered with these methods is that the size of thefilter required for an effective noise reduction grows in proportion tothe amount of noise in the image. However, the size of the filters isusually limited in order to save hardware costs, and softwareimplementations tend to incur too much time and processing power to bepractical. Suppressing the chromatic components of the pixels to zero indark or gray areas reduces the chromatic component of the noise in theseareas. The difficulty encountered using this method is that it affectsonly dark/gray areas, and it is also very likely to suppress real colorsin the image. A seminal article on aspects of noise reduction in imageryand using sigma filters for this purpose is given in “Digital ImageSmoothing and the Sigma Filter”, Lee, J. S., Computer Vision, Graphics,and Image Processing, 24, 255-269, 1983.

These various prior art methods tend to have a number of shortcomingswhen it comes to implementation in digital cameras, video, and otherimaging systems. There will always be noise when an image is captured inlow light conditions. The noise level will increase as the sensor pixelsize is decreased due to sensor resolution issues and due to a trend toreduce sensor cost. Therefore, there is substantial room forimprovements in digital imaging systems, even when considering futurechanges in the application environment.

SUMMARY

The described methods and corresponding apparatus provide ways toachieve superior image quality as compared to previous noise reductionapproaches. A noise reduction apparatus is presented that includesgroups of one or more serially connected non-linear filter unit groups.Each of the filter unit groups are driven by decimated input image dataat a different level of decimation and the output of at least one ofthese groups serves as one of a plurality of inputs to another groupdriven at a different decimation level.

Various aspects, advantages, features and embodiments of the presentinvention are included in the following description of illustrativeexamples thereof, which description should be taken in conjunction withthe accompanying drawings. To the extent of any inconsistency orconflict in the definition or use of terms between any of the patents,patent applications, articles, other publications, documents and thingsreferenced herein, those of the present application shall prevail.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a filter unit group of the presentinvention;

FIG. 2 is a block diagram of one configuration (“increasing depth” mode)of filter unit groups;

FIG. 3 is a block diagram of a second configuration (“increasing width”mode) of filter unit groups;

FIG. 4 shows a generalized filter array combining the width and depthmodes; and

FIG. 5 illustrates an example of an extended filter unit group of thepresent invention.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS Overview

In order to achieve the desired results, illustrative embodimentsdescribed herein employ a plurality of small noise reduction non-linearfilters working on decimated (downscaled) representations of the imagebeing acquired and performing in concert to achieve performanceequivalent to a much larger filter. These offer a comprehensivesolution, which can be implemented in hardware (HW) in the illustrativeembodiments, and which includes decimation along with small sizenon-linear low pass filters. The results obtained can achieve qualitysuch as that obtained by applying a much larger filter, with only aminor penalty in motion image frame rate performance or silicon area.

The illustrative embodiments provide a comprehensive solution fordigital cameras, video, and imaging systems that provides effectivenoise reduction in an image acquired using a high gain analog signalpath between the imaging system's sensor and its analog to digital (A/D)converter. Images acquired under these conditions are often call “highISO images”. In this imaging environment, the various aspects presentedin the following provide results equivalent in quality to those achievedby very large noise reduction filters, without the cost in frame-rateand/or DRAM-bandwidth that is usually incurred when such filters areemployed.

A particular advance in the state of the art can be stated as thetechnique of using an array of filters (of any appropriate type), atvarious levels of decimation (downscaling to lower resolution), and theuse of adaptive combining of filtered image data, dependent on one ormore image metrics related to one or more local regional imagecharacteristics. For example, this combining operation could be ablending operation that is responsive to a flatness measure. One suchflatness measure could be the spatial uniformity of the local region ofthe image being processed. A second could be the chromaticity uniformityof the local region of the image being processed.

These techniques provide effective noise removal and higher imagequality for high ISO images, with minor costs in required hardware,frame rates and DRAM bandwidth. It allows the use of sensors and/or ananalog front end with lower SNR qualities, such as CMOS sensors, orsensors with higher resolution and smaller pixel size. Further, itallows further increase in the ISO sensitivities in camera products, andincreases the ability of cameras to capture images in low lightconditions.

Illustrative Filter Arrangements

The described techniques are based on successive decimation of theneighborhood of the current pixel, edge preserving (or, more generally,non-linear) filtration of the decimated pixel data, and interpolationand blending of the various decimated image data to achieve superiornoise reduction without incurring excessive cost in terms of hardware,processing power or time. The implementation described here is based ona hardware solution; however, it will be clear to one skilled in the artthat the same concepts can be implemented in software, firmware, or acombination of these running on an appropriate signal processor, withassociated memory and peripheral hardware.

A basic component of the described noise reduction system is the “FilterUnit Group”. This component is shown in FIG. 1 as block 101. Filter unitgroups are connected together to form the array of filters, previouslydiscussed. In its simplest form, a filter unit group includes twomodules: a Noise Reduction Non-linear Filter Element 111, and a BlendModule 121. The Noise Reduction Non-linear Filter Element 111selectively removes noise from the image while substantially preservingthe edge detail in the image. Additionally, the filter creates one ormore metrics that provide an indication of one or more local regionalimage data characteristics. For example, one such metric may be“flatness_measure(x,y)” of FIG. 1, which is a measure of the spatialuniformity of the local region of the image data being processed.Another may be a Dynamic Range Compensation (DRC) gain factor, which isa measure of the visibility of each pixel, as compared to neighboringpixels, in the local region of the image data being processed. Such again factor is described, for example, in issued U.S. Pat. No. 7,899,267entitled “Dynamic range compensation by filter cascade” by Itsik Dvirissued Mar. 1, 2011, which is incorporated herein by reference,particularly (but not exclusively) column 2 lines 7-31, column 2 lines59-67, column 3 lines 1-19, column 3 lines 31-51, column 4 lines 9-24and FIG. 2. A third such metric may be a measure of the chromaticity ofeach pixel in the local region of the image data being processed. Afourth may be a measure of the chromaticity uniformity of each pixel inthe local region of the image data being processed. In response to oneor more of these metrics, the Blend Module 121 melds (at 127) thecurrent noise filtered pixel with image data impressed on its secondaryinput (at the bottom of the module, labeled ‘upscaled_pix(x,y)’). Thedescribed illustrative embodiment of the present invention's blendmodule employs a single flatness measure metric, and forms a linearcombination weighted (at 123 and 125) by the “flatness_measure(x,y)”.The operation of an illustrative Noise Reduction Non-linear FilterElement is described in greater detail later in this discussion. Thoseskilled in the art will recognize that in the illustrative embodimentany Noise Reduction Non-Linear Filter Element 111 that preserves edgesand also provides a suitable metric of local image uniformity may beused in place of the filter describe herein. Further, it will be readilyrecognized by those skilled in the art that metric creation operationscan be separated from noise reduction non-linear filtering operations,thus allowing one or more separate metric creation modules to beincluded in a filter unit group, along with a dedicated noise reductionnon-linear filter element.

At each pixel location in the image, the current pixel is cleansed ofnoise by examination and processing of the current region's pixel data(neighborhood). The neighborhood is defined in the illustrativeembodiments as the pixels in an N×N square of data surrounding thecurrent pixel, where N is a small odd integer (five, for example).

The enhanced noise reduction operation of the system is achieved byconfiguring a plurality of filter unit groups, using decimation(downscaling), filtering, interpolation (upscaling) of the filteredpixel data and blending the upscaled data with higher resolution data.Several configurations are possible, with some examples described withrespect to FIGS. 2-4.

FIG. 2 details one such configuration, dubbed “Increasing Depth” modehere. In this mode, the full scale pixel data is decimated to severallower levels of resolution, by decimate blocks 205, 215, and 225. Ateach level, the decimated data is filtered, and then selectivelyblended, by filter unit groups 101, 201, and 211, with upscaled dataoutput from interpolate blocks 207, 217, and 227. Immediately lowerresolution output from filter unit groups 201, 211 and 221 driveinterpolate blocks 207, 217, and 227 respectively. The non-linear filterelements of filter unit groups 101, 201, 211, and 221, shown in FIG. 2as processing increasingly lower resolution image data, can all employthe same filtering algorithm to effect noise reduction. However, theycan also employ different filtering algorithms based upon variouscriteria. One such criteria could be the resolution of the image databeing processed. For example, the effectiveness of the non-linear filterelement incorporated in filter unit group 221, which operates on imagedata that has been decimated to the lowest level of image resolutiondepicted in FIG. 2, may be improved by taking into account the lowerresolution level of the image data being processed. The blendingprocesses employed by filter unit groups 101, 201, 211, and 221 areresponsive to a metric that indicates a local regional image datacharacteristic. One such metric may be an indication of the uniformityof a local region of the image data being processed. This metricprovides a measure of the degree of edge activity detected at eachresolution level and thus can be employed by the blending process toprevent edges from losing sharpness. Any number of resolution levels canbe used as indicated by the dotted line extensions. The number ofresolution levels will be limited by the size of the image and theamount of scaling between one resolution level and the next. (Additionaldetail on upscaling and downscaling can be found, for example, in U.S.Pat. No. 6,937,772, which employs a differing filtering arrangement.)The amount of upscaling at a given level's interpolation will typicallybe the same as the amount of downscaling at that level's decimation.

The configuration shown in FIG. 2 can pass metrics from one filter unitgroup at one downscaling level, to a second filter unit group at adifferent downscaling level, in order to improve the filteringcapabilities of the second filter unit group. For example, if a filterunit group is operating at a downscaling level of 1:2, such as FilterUnit Group 201. and another filter unit group is operating at adownscaling level of 1:4, such as Filter Unit Group 211, a flatnessmeasure, or one or more other metrics, calculated in Filter Unit Group211 can be passed to Filter Unit Group 201 over line 235. Filter UnitGroup 201 can then consider the metrics provided by Filter Unit Group211 when performing filtering and calculation of its own metrics.Similarly, metrics created in Filter Unit Group 201 can be passed to a1:1 filter unit group, such as Filter Unit Group 101, over line 240,making it possible for Filter Unit Group 101 to consider metricsprovided by Filter Unit Group 201 when performing filtering andcalculation of its metrics. When passing metrics created in a filterunit group at one downscaling level to a filter unit group at adifferent downscaling level, it may be necessary to scale the metrics tothe downscaling level of the receiving filter unit group in order forthe metrics to be used. The need to scale the metric will depend on thenature of the metric being passed and how the receiving filter unitgroup employs the metric in its processing operations.

FIG. 3 details another possible configuration, dubbed “Increasing Width”mode here. In “Increasing Width” mode processing, the upscaled_pix(x,y)input to the filter unit groups of the bottom row are unused andunconnected. Therefore, no blending is performed along the bottom row,with only the Noise Reduction Non-linear Filter Element portion of thefilter unit groups being used. The width mode may have any number ofinstances of filter unit groups in series. FIG. 3 shows two such FilterUnit Groups, 201 a and 201 b, where any additional filter unit groupswould be placed in between. The first of these, 201 a, receives theinput decimated at 205 and the last (or, here second) Filter Unit Group201 b then has its output upscaled at 207 to serve as the second inputof the top layer Filter Unit Group 101. Because the noise reductionnon-linear filter elements are edge preserving, repeated applications ofthe same filter on the same data set will provide more and more refinedestimates of the noise free value of the current pixel. Note that as theedge preserving filter stages are non-linear, using multiple suchfilters serially will act differently than just a single such stage withmore aggressive filtering.

The system can use both “Width” and “Depth” modes simultaneously, asdiagrammed in FIG. 4. In this approach, filter unit groups are connectedas an array with a depth of several rows, where each row uses the“width” mode arrangement. The first row, operating at a 1:1 downscalinglevel, is shown in FIG. 4 as having only one filter unit group, but itmay also employ an array of filter unit groups in a width modearrangement. (In FIG. 4, as well as in the other figures, thearrangement of filter unit groups is a schematic to show the functionalrelationships among the various system components, whether these areimplemented in software or hardware. Thus, in a hardware or softwareimplementation, the various components need not be physically arrangedas shown.) In the illustrated embodiment, several levels of decimationare provided, which, at each level, are subjected to noise reductionnon-linear filters without blending (“Width” mode processing), afterwhich upscaled versions of the resulting image data are blended with theimmediately higher resolution image data (“Depth” mode processing). Eachlevel has multiple filter unit groups (201 i, 211 i, 221 i) connected inseries, where the first of the series receives input decimated (at 205,215, 225) with respect to the level above, and supplies the output ofthe last of the series (interpolated at 207, 217, 227) to the last ofthe series in the level above. This matrix of filters is limited only bythe size of the neighborhood considered, and the processing power andtime available. Although the illustrative embodiment shows the samenumber of filter unit groups in each row, after the first single filterrow, more generally this need not be the case.

Consequently, it can be seen that the present invention is comprised ofan aggregation of one or more filter unit groups connected serially,with each of these aggregates being driven by input image data decimatedto a different level of downscaling. One or more of the outputs of atleast one of the filter unit groups of a first aggregate, may then serveas one or more inputs to at least one of the filter unit groups of asecond aggregate at a different decimation level.

Filter Unit Group Operation

For each Input Image pixel in x,y coordinates, Pin(x,y), an illustrativeembodiment of the filter unit group performs a series of operations. Thefirst of these is using input image pixels in an N×N environment aroundPin(x,y) to perform edge preserving low pass filtering. The result isPin_filtered(x,y). N is typically a small integer number. Thepseudo-code description below gives the operation of an illustrativefilter.

Also using the input image in an N×N environment around Pin(x,y), theuniformity metric which indicates the flatness of the environment aroundPin(x,y) is calculated. The result is Flatness_measure(x,y). N istypically a small odd integer number (five, for example).

In the instance that the filter unit group also receives an up-scaleddecimated image (“Depth” mode operation), the module blendsPin_filtered(x,y) with the equivalent pixel from the upscaled decimatedimage, Upscaled_pix(x,y), according to the uniformity metric,Flatness_measure(x,y), to get the Output Image pixel, Pout(x,y):

Pout(x,y)=Flatness_measure(x,y)*Upscaled_pix(x,y)+(1−Flatness_measure(x,y))*Pin_filtered(x,y)

Flatness_measure(x,y) is a value within the range of [0,1]. Around edgesit tends toward 0, resulting in milder filtering. In flat areas of theInput Image, it is pushed towards higher values closer to 1, resultingin more aggressive filtering.

An Illustrative Filter Unit Group

This section presents a pseudo-code description of the operation of anillustrative non linear filter element. The filter described is of theclass of sigma filters, however, those skilled in the art will recognizethat any noise reducing filter that also provides an estimate of thelocal region's spatial uniformity may be used for this function.

In this description, the constant “T” is a threshold that indicates thenoise levels of the camera's sensor and changes according to the ISOsensitivity for each possible shutter setting. For a given camera and agiven ISO setting, the camera manufacturer can calculate T by, forexample, capturing a color chart and then determining the maximum valueof abs(Pin[x,y]−Pin[x+n,y+m]) over all the flat areas in the image(areas without edges or transitions).

In order to execute an N×N filter (where N is an odd integer) on eachinput image pixel in the stream Pin(x,y), an illustrative embodimentperforms the following:

  {  Sum[x,y] = 0  Count[x,y] = 0  for (m= -(N-1)/2; m<=(N-1)/2;m++)  {  for (n= -(N-1)/2; n<+ (N-1);n++)   {    if (abs(Pin[x,y] −Pin[x+n,y+m]) < T)    {      Sum[x,y] = Sum[x,y] + Pin[x+n,y+m]     Count[x,y] = Count[x,y] + 1    }   }  }  If (filtering isperformed)   Pin_filtered[x,y] = Sum[x,y]/Count[x,y]  Else  Pin_filtered[x,y] = Pin[x,y]  Flatness_measure[x,y] = Count[x,y]/(N²) If (Flatness_measure[x,y] < A)   Flatness_measure[x,y] = 0  Else  Flatness_measure[x,y] = (Flatness_measure[x,y]-A)/(1-A) }In the above pseudo-code, Parameter A is a threshold below which an areawill be considered as not flat. This allows the flatness_measure to beassigned the value 0, preventing blending from being performed on edges.Such blending can damage the edges and cause the image to losesharpness.

Implementation

In one illustrative embodiment of the invention, the matrix offilter/blend operations is achieved by successive iterations of a singlefilter unit group described above with respect to Figure I. In order toimplement the generalized filter array described in FIG. 4 above, thesystem could proceed with the following sequence of actions:

Decimate the full-scale image successively to the depth of resolutiondesired, storing each decimated image individually.

Beginning at the lowest resolution, apply noise reduction successively,without the blending function (“Width” mode processing), to the desirednumber of iterations.

Upscale the current resolution to the next higher level and store it.

At the next higher resolution, apply noise reduction successively,without the blending function, to the desired number of iterations.

Blend the resulting image with the upscaled image from the next lowerresolution level.

Repeat steps 3, 4, and 5 until a full-scale image is achieved.

As previously discussed, the basic component of the noise reductionsystem of the present invention is the filter unit group. It is shown in101 of FIG. 1 as being comprised of two modules: The Noise ReductionNon-linear Filter Element 111, and a Blend Module 121. This structuremay be extended by adding noise reduction filters, as well as otherimage processing modules after the blend module as well as before. Theother image processing modules need not be directly related to a noisereduction function. In this extended configuration, it is the output ofthe final module or filter element in the series, after a blendingoperation, that drives a second filter unit group. In the previouslydiscussed filter unit group configuration of FIG. 1, at least one metricis created by either one or more separate metric creation modules, or bythe one or more non-linear filter elements. Recall that these metricsindicate at least one local regional input image data characteristic andare used by the filter unit group's blend module to improve theeffectiveness of the blending operation. In the extended filter unitgroup configuration, these metrics can also be used by the other imageprocessing modules as part of their processing operations to improveprocessing effectiveness. The other processing modules of the extendedfilter group configuration could be incorporated between a non-linearfilter element and a blend module, before a non linear filter element,between a non-linear filter element and a second non-linear filter, orafter a blend module. These processing modules may, for example, provideedge enhancement and/or color transformations. FIG. 5 illustrates onepossible extended filter unit group configuration of the presentinvention. It is comprised of Edge Enhancement Processor 507, betweentwo Noise Reduction Non-linear Filter Elements, 511 and 517, the outputof Noise Reduction Non-linear Filter Element 517 driving Blend Module521. Blend Module 521 is followed by a third Noise Reduction Non-linearFilter Element, 531. The output of Noise Reduction Non-linear FilterElement 531, appearing on line 541, may be input to a second filter unitgroup at a higher level of resolution, after the image data on line 541is interpolated to the same level of resolution as the that used by thesecond filter unit group. Therefore, as one illustration, the image dataon line 541 after interpolation may be used to drive theUpscaled_pix(x,y) input of FIG. 1, and be input to the filter blendmodule of Filter Unit Group 101, shown in FIGS. 1 and 2. In addition,the flatness measure metrics calculated in Noise Reduction Non-linearFilter Element 531, Flatness_measure(x,y) and 1−Flatness_measure(x,y),can be input over line 240 of FIG. 2, to Filter Unit Group 101, in orderto improve the filtering properties of Filter Unit Group 101. In thisillustration, the Filter Unit Group 201 of FIG. 2 is configured as anextended filter unit group whose image data output is interpolated tothe same level of resolution as Filter Unit Group 101 by InterpolateBlock 207. Filter Unit Group 101 can also be configured as an extendedfilter unit group. The structure of an extended version of Filter UnitGroup 101 may be the same as the structure employed by extended FilterUnit Group 201, but this need not be the case. Filter Unit Group 101 mayincorporate different image processing modules, or the same processingmodules in a different order, after the blend module as well as before.In general, the processing elements employed by an extended filter groupare not necessarily the same at each down scaling level, though theycould be. Further, each of the extended filter unit groups connectedserially to form an aggregate of extended filter unit groups at aparticular down scale level need not employ the same processingelements, or order of processing elements, although they could.

The present invention is applicable to color image data. For color imagedata the noise reduction non-linear filter element of FIG. 1 wouldcreate at least one metric for each color or channel of the input imagedata. These metrics could also be created in one or more separate metriccreation modules. The blend module would receive filtered image datafrom the non-linear filter element, at least one metric for each coloror channel of the input image data, and image data output from a secondfilter unit group, and output a noise reduced combination image dataoutput that is responsive to the metrics. In the case of color imagedata encoded in the Red, Green, and Blue (RGB) color image format, thenoise reduction approach of the present invention would be appliedseparately to image data pixels of the same color. In the case of colorimaged data encoded in the YUV color image format, the noise reductionapproach of the present invention could likewise be applied to the colorimage data's luma channel (Y) and chroma channels (U,V) separately.However, for a color image encoded in the YUV color image format, thenoise reduction approach of the present invention may be more effectiveif the noise reduction scheme applied to the chroma channels isdependent on the noise reduction scheme applied to the luma channel.This would mean that the noise filtering process and regional imagemetric calculation applied to U,V channels of image data are responsiveto the noise filtering process and regional image metric calculationapplied to the Y channel image data. This approach may improve thepresent invention's noise reduction processing effectiveness, however,it is not necessary for the proper operation of the present invention.

CONCLUSION

Although the various aspects of the present invention have beendescribed with respect to illustrative embodiments thereof, it will beunderstood that the present invention is entitled to protection withinthe full scope of the appended claims.

1. An image noise reduction apparatus comprising: a plurality of filterunit groups, each of said plurality of filter unit groups configured toindependently suppress noise in input image data; wherein a first filterunit group of the plurality of filter unit groups includes: a noisereduction non-linear filter element to receive input image data andcreate filtered image data output; a first metric creation module toreceive input image data and create a first metric, said first metricindicating a first local regional input image data characteristic; and ablend module to receive the filtered image data, the first metric, andimage data output from a second filter unit group of the plurality offilter unit groups, and output a noise reduced combination image dataoutput, wherein the noise reduced combination image data output isresponsive to the first metric.
 2. The image noise reduction apparatusof claim 1 comprising a plurality of metric creation modules, includingsaid metric creation module, each to create a separate metric.
 3. Theimage noise reduction apparatus of claim 1 wherein the metric is anindication of the spatial uniformity of a local region of the inputimage data.
 4. The image noise reduction apparatus of claim 1 whereinthe metric is an indication of the chromaticity of a local region of theinput image data.
 5. The image noise reduction apparatus of claim 1wherein the metric is an indication of the chromaticity uniformity of alocal region of the input image data.
 6. The image noise reductionapparatus of claim 1 wherein the metric is an indication of pixelvisibility in a local region of the input image data as is in the formof pixel by pixel gain factors.
 7. The image noise reduction apparatusof claim 1 wherein the image data output of said second filter unitgroup is upscaled to a resolution that matches the resolution of thefiltered image data.
 8. The image noise reduction apparatus of claim 1wherein the noise reduction non-linear filter element and the at leastone metric creation module are combined in a single element.
 9. Theimage noise reduction apparatus of claim 1 wherein the input image dataand the image data from the second filter unit group is encoded in thered, green and blue color image format; the noise reduction non-linearfilter element processes the input image data of each color separatelyto output separate red, green, and blue filtered color image data; theat least one metric creation module creates at least one metric for eachcolor of the input image data; and the blend module respectively blendsthe filtered color image data with color image data from the secondfilter group in response to the at least one metric for each color, tocreate a noise reduced color combination image data output.
 10. Theimage noise reduction apparatus of claim 1 wherein the input image dataand the image data from the second filter unit group is encoded in theYUV color image format; the noise reduction non-linear filter elementprocesses the input image luma channel (Y) and chroma channels (U,V)data separately to output separate luma and chroma filtered image data;the at least one metric creation module creates at least one metric foreach channel of the input image data; and the blend module respectivelyblends the separate luma and chroma filtered image data with luma andchroma image data from the second filter group in response to the atleast one metric for each channel, to create a noise reduced YUV colorimage format combination image data output.
 11. The image noisereduction apparatus of claim 10 wherein the noise reduction processapplied to the chroma channels of image data are dependent on the noisereduction process applied to the luma channel of image data.
 12. Theimage noise reduction apparatus of claim 1 wherein a first aggregate ofconnected filter unit groups receives downscaled input image data at adifferent level of downscaling than a second aggregate of seriallyconnected filter unit groups; an output of said first aggregate servesas an input to said second aggregate, wherein the image data output ofthe first aggregate is upscaled to a resolution that matches thedownscaling level of the second aggregate prior to being output to thesecond aggregate; and a metric is passed from said first aggregate tosaid second aggregate for use by a filter unit group of the secondaggregate when the filter unit group of the second aggregate isperforming filtering and metrics calculations.
 13. The noise reductionapparatus of claim 1, wherein the non-linear filter elements of thefilter unit groups are implemented in hardware.
 14. The noise reductionapparatus of claim 1, wherein the non-linear filter elements of thefilter unit groups are implemented in software.
 15. The noise reductionapparatus of claim 1, wherein the non-linear filter elements of thefilter unit groups use sigma class filters.
 16. The noise reductionapparatus of claim 1, wherein the non-linear filter elements of thefilter unit groups use the same filtering algorithm.
 17. The noisereduction apparatus of claim 1, wherein the non-linear filter elementsof the filter unit groups use different filtering algorithms.
 18. Thenoise reduction apparatus of claim 1, wherein the input image data isimage data from a digital camera.
 19. The noise reduction apparatus ofclaim 1, wherein the input image data is image data from a video camera.20. The image noise reduction apparatus of claim 1 wherein at least oneof said plurality of filter unit groups includes a plurality of noisereduction non-linear filter elements.
 21. The image noise reductionapparatus of claim 1 including at least one image processing module. 22.The image noise reduction apparatus of claim 21 wherein the imageprocessing module is an edge enhancement processing module.
 23. Theimage noise reduction apparatus of claim 21 wherein the image processingmodule is a color transformation processing module.
 24. The image noisereduction apparatus of claim 21 wherein the image processing module usesa metric as part of its processing operation.
 25. A method of performinga noise reduction operation on input image data, comprising:independently generating a first noise reduced non-linear filtered imagedata output from the input image data; generating a first metricindicating a first local regional characteristic of the input imagedata; and blending the first noise reduced non-linear filtered imagedata with independently generated second noise reduced non-linearfiltered image data in response to the first metric.
 26. The noisereduction method of claim 25 wherein a plurality of metrics aregenerated, including said first metric.
 27. The image noise reductionmethod of claim 25 wherein the metric is an indication of the spatialuniformity of a local region of the input image data.
 28. The imagenoise reduction method of claim 25 wherein the metric is an indicationof the chromaticity of a local region of the input image data.
 29. Theimage noise reduction method of claim 25 wherein the metric is anindication of the chromaticity uniformity of a local region of the inputimage data.
 30. The image noise reduction method of claim 25 wherein themetric is an indication of the pixel visibility in a local region of theinput image data and is in the form of pixel by pixel gain factors. 31.The image noise reduction method of claim 25 wherein the second noisereduced non-linear filtered image data is upscaled to a resolution thatmatches the resolution of the first noise reduced non-linear filteredimage data.
 32. The image noise reduction method of claim 25 wherein theinput image data and the second noise reduced non-linear filtered imagedata are encoded in the red, green and blue color image format; noisereduced non-linear filtered image data are created for each color; ametric is created for each input image data color; and the first noisereduced non-linear filtered image data is blended with the second noisereduced non-linear filtered image data in response to the metrics. 33.The image noise reduction method of claim 25 wherein the input imagedata and the second noise reduced non-linear filtered image data areencoded in the YUV color image format; first noise reduced non-linearfiltered image data are created from the input image luma channel (Y)and chroma channels (U,V) data separately; a metric is created for eachinput image data channel; and the first noise reduced non-linearfiltered image data is blended with the second noise reduced non-linearfiltered image data in response to the metrics.
 34. The image noisereduction method of claim 33 wherein first noise reduced non-linearfiltered chroma channel image data creation is dependent on first noisereduced non-linear filtered luma channel image data creation.